1. Field of the Invention
The present invention generally relates to systems and methods for analyzing text, and more particularly to automated systems, methods and computer program products for facilitating the reading, analysis and scoring of text.
2. Related Art
In today's technological environment, many automated tools are known for analyzing text. Such tools include systems, methods and computer program products ranging from spell checkers to automated grammar checkers and readability analyzers. That is, the ability to read text in an electronic form (e.g. in one or more proprietary word processing formats, ASCII, or an operating system's generic “plain text” format), parse the inputted text—determining the syntactic structure of a sentence or other string of symbols in some language, and then compare the parsed words to a database or other data repository (e.g., a dictionary) or set of rules (e.g., English grammar rules) is known. This is true for text in different languages and regardless of whether that text is poetry or prose and, if prose, regardless of whether the prose is a novel, an essay, a textbook, a play, a movie script, a short manifesto, personal or official correspondence, a diary entry, a log entry, a blog entry, or a worded query, etc.
Some systems have gone further by attempting to develop artificial intelligence (AI) features to not only process text against databases, but to automate the “understanding” of the text itself. However, developing such natural language processing and natural language understanding systems has proven to be one of the most difficult problems within AI, due to the complexity, irregularity and diversity of human language, as well as the philosophical problems of meaning. More specifically, the difficulties arise from the following realities: text segmentation (e.g., recognizing the boundary between words or word groups in order to discern single concepts for processing); word sense disambiguation (e.g., many words have more than one meaning); syntactic ambiguity (e.g., grammar for natural languages is ambiguous, and a given sentence may be parsed in multiple ways based on context); and speech acts and plans (e.g., sentences often do not mean what they literally may imply).
In view of the above-described difficulties, there is a need for systems, methods and computer program products for facilitating the automated analysis of text. For example, publishing houses often receive large numbers of manuscripts from various authors seeking publishing contracts. The sheer volume of submissions (solicited and unsolicited) prevents publishing house personnel from physically being able to read each of the submissions. Consequently, for example, manuscripts that contain well-written stories which may be commercially successful, never make it through the review process.
Given the foregoing, what is needed is a system, method and computer program product for facilitating the automated reading, analysis and scoring of text. That is, for example, an automated tool to assist publishing house personnel to quickly “read” submitted manuscripts and score their quality would be desirable.
The need to facilitate automated reading of text goes beyond publishable manuscripts and into fragments of manuscripts and even smaller blocks of text in standard data formats, such as photograph captions and other elements of PDF format documents HTML web pages. To index and retrieve the meaning of these smaller units of text, Google and other search engine companies have devoted significant resources to creating keyword and phrase indices, with some semantic processing to group indexed text into semantically coherent ontological categories, for example. However, usable meanings of text are not confined to dry ontological semantics. Indeed, often the most useful meaning of text is a matter of emotional mood, which greatly influences textual meanings. From a human cognitive standpoint, it is well understood that children initially develop a foundation of emotional memories, concerning needs and curiosity, from which ontological memories are later developed. There is a similar need for the automated reading of text to proceed from a foundation of emotional references, in order to cohere a framework of retrievable text consistent with a human cognitive viewpoint. Building a framework of retrievable text upon dryly emotionless ontologies deviates considerably from natural human values, so much so that the resulting index may be several interfaces removed from natural human thought, requiring query and browsing interfaces to convert results into useful thoughts. An automated reading of text built upon a framework of human emotions would be more efficient, as the emotional desires of a user could be connected directly to an index of matching emotional text.